Real-Time Underwater Fish Tracking Based on Adaptive Multi-Appearance Model
Xiaojing Li, Zhiqiang Wei, Lei Huang, Jie Nie, Wenfeng Zhang, Lu Wang · 2018
Tracking live fish in an open underwater environment to investigate their behavior is of great value for many applications, e.g. biological and robotic research. However, tracking fish in real world environment is a challenging task due to complex non-rigid deformation and abrupt movement of fish. In this paper, we explore and incorporate motion property of fish and propose a real-time fish tracking method based on novel adaptive multi-appearance models and tracking strategy, which can be adapted to various changes of the fish appearance caused by non-rigid deformation. Experimental results show the promising performance of the proposed method can outperform the previous method by 13.4% in accuracy on average and is robust to real-time underwater fish tracking.